How a Chinese Company Swept the Embodied Intelligence Olympics with Faster, Precise, Low‑Data Robotics

A Chinese robotics firm leveraged a self‑developed VLA model to win all three core tasks at Benjie’s Embodied Intelligence Olympics—peeling oranges, unlocking doors, and flipping socks—outperforming the industry leader Physical Intelligence by up to 35% faster speed, using 30% fewer samples and achieving higher precision in real‑world, fully autonomous scenarios.

Embodied AIRoboticsVLA model

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How a Chinese Company Swept the Embodied Intelligence Olympics with Faster, Precise, Low‑Data Robotics

Data Party THU

Nov 4, 2025 · Artificial Intelligence

Why Evolution Strategies Beat Reinforcement Learning for Large‑Model Fine‑Tuning

This article reviews the paper “Evolution Strategies at Scale: LLM Fine‑Tuning Beyond Reinforcement Learning”, explaining how parameter‑space exploration via ES provides more stable, sample‑efficient, and reproducible fine‑tuning for billion‑parameter LLMs such as Qwen‑2.5 and LLaMA‑3, and detailing the algorithmic and engineering innovations that make full‑parameter ES practical.

Evolution StrategiesParameter Space OptimizationScalable Training

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Why Evolution Strategies Beat Reinforcement Learning for Large‑Model Fine‑Tuning

Data Party THU

Oct 15, 2025 · Artificial Intelligence

Designing Safe, Sample-Efficient, and Robust Reinforcement Learning for Ranking and Diffusion Models

This paper proposes a reinforcement‑learning framework that simultaneously ensures safety, sample efficiency, and robustness, applying a contextual‑bandit perspective to ranking/recommendation systems and text‑to‑image diffusion models, and introduces novel algorithms for safe deployment, variance‑reduced off‑policy estimation, and a LOOP method for generative RL.

Diffusion ModelsReinforcement LearningRobustness

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Designing Safe, Sample-Efficient, and Robust Reinforcement Learning for Ranking and Diffusion Models

DataFunSummit

Jul 8, 2024 · Artificial Intelligence

World Models and Causal Inference in Reinforcement Learning: A Comprehensive Overview

This article reviews the role of world (mental) models and causal inference in reinforcement learning, covering their theoretical foundations, model‑based RL frameworks such as Dyna, sample‑efficiency challenges, causal structure learning, distribution correction, dynamics‑reward modeling, and experimental results that demonstrate performance gains across multiple tasks.

Reinforcement LearningWorld Modelscausal inference

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World Models and Causal Inference in Reinforcement Learning: A Comprehensive Overview